459 research outputs found
Emergent Phases of Nodeless and Nodal Superconductivity Separated by Antiferromagnetic Order in Iron-based Superconductor (Ca4Al2O6)Fe2(As1-xPx)2: 75As- and 31P-NMR Studies
We report P- and As-NMR studies on
(CaAlO)Fe(AsP) with an isovalent substitution
of P for As. We present the novel evolution of emergent phases that the
nodeless superconductivity (SC) in 00.4 and the nodal one around
=1 are intimately separated by the onset of a commensurate stripe-type
antiferromagnetic (AFM) order in 0.5 0.95, as an isovalent
substitution of P for As decreases a pnictogen height measured from
the Fe plane. It is demonstrated that the AFM order takes place under a
condition of 1.32\AA1.42\AA, which is also the case for other
Fe-pnictides with the Fe state in (Fe) layers. This novel
phase evolution with the variation in points to the importance of
electron correlation for the emergence of SC as well as AFM order.Comment: 5pages, 4figures; accepted for publication as a Rapid Communication
in Phys. Rev.
High-Tc Nodeless s_\pm-wave Superconductivity in (Y,La)FeAsO_{1-y} with Tc=50 K: 75As-NMR Study
We report 75As-NMR study on the Fe-pnictide high-Tc superconductor
Y0.95La0.05FeAsO_{1-y} (Y0.95La0.051111) with Tc=50 K that includes no magnetic
rare-earth elements. The measurement of the nuclear-spin lattice-relaxation
rate 75(1/T1) has revealed that the nodeless bulk superconductivity takes place
at Tc=50 K while antiferromagnetic spin fluctuations (AFSFs) develop moderately
in the normal state. These features are consistently described by the multiple
fully-gapped s_\pm-wave model based on the Fermi-surface (FS) nesting.
Incorporating the theory based on band calculations, we propose that the reason
that Tc=50 K in Y0.95La0.051111 is larger than Tc=28 K in La1111 is that the FS
multiplicity is maximized, and hence the FS nesting condition is better than
that in La1111.Comment: 4 pages, 3 figures, accepted for publication in Phys Rev. Let
Gradient descent learning in and out of equilibrium
Relations between the off thermal equilibrium dynamical process of on-line
learning and the thermally equilibrated off-line learning are studied for
potential gradient descent learning. The approach of Opper to study on-line
Bayesian algorithms is extended to potential based or maximum likelihood
learning. We look at the on-line learning algorithm that best approximates the
off-line algorithm in the sense of least Kullback-Leibler information loss. It
works by updating the weights along the gradient of an effective potential
different from the parent off-line potential. The interpretation of this off
equilibrium dynamics holds some similarities to the cavity approach of
Griniasty. We are able to analyze networks with non-smooth transfer functions
and transfer the smoothness requirement to the potential.Comment: 08 pages, submitted to the Journal of Physics
Functional Optimisation of Online Algorithms in Multilayer Neural Networks
We study the online dynamics of learning in fully connected soft committee
machines in the student-teacher scenario. The locally optimal modulation
function, which determines the learning algorithm, is obtained from a
variational argument in such a manner as to maximise the average generalisation
error decay per example. Simulations results for the resulting algorithm are
presented for a few cases. The symmetric phase plateaux are found to be vastly
reduced in comparison to those found when online backpropagation algorithms are
used. A discussion of the implementation of these ideas as practical algorithms
is given
Lobby index as a network centrality measure
We study the lobby index (l-index for short) as a local node centrality
measure for complex networks. The l-inde is compared with degree (a local
measure), betweenness and Eigenvector centralities (two global measures) in the
case of biological network (Yeast interaction protein-protein network) and a
linguistic network (Moby Thesaurus II). In both networks, the l-index has poor
correlation with betweenness but correlates with degree and Eigenvector. Being
a local measure, one can take advantage by using the l-index because it carries
more information about its neighbors when compared with degree centrality,
indeed it requires less time to compute when compared with Eigenvector
centrality. Results suggests that l-index produces better results than degree
and Eigenvector measures for ranking purposes, becoming suitable as a tool to
perform this task.Comment: 11 pages, 4 figures. arXiv admin note: substantial text overlap with
arXiv:1005.480
On the robustness of scale invariance in SOC models
A random neighbor extremal stick-slip model is introduced. In the
thermodynamic limit, the distribution of states has a simple analytical form
and the mean avalanche size, as a function of the coupling parameter, is
exactly calculable. The system is critical only at a special point Jc in the
coupling parameter space. However, the critical region around this point, where
approximate scale invariance holds, is very large, suggesting a mechanism for
explaining the ubiquity of scale invariance in Nature.Comment: 6 pages, 4 figures; submitted to Physical Review E;
http://link.aps.org/doi/10.1103/PhysRevE.59.496
On the random neighbor Olami-Feder-Christensen slip-stick model
We reconsider the treatment of Lise and Jensen (Phys. Rev. Lett. 76, 2326
(1996)) on the random neighbor Olami-Feder-Christensen stik-slip model, and
examine the strong dependence of the results on the approximations used for the
distribution of states p(E).Comment: 6pages, 3 figures. To be published in PRE as a brief repor
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